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    "import sys\n",
    "import os\n",
    "if not any(path.endswith('textbook') for path in sys.path):\n",
    "    sys.path.append(os.path.abspath('../../..'))\n",
    "from textbook_utils import *"
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    "dogs = pd.read_csv('data/akc.csv')\n",
    "\n",
    "kids = {1:\"high\", 2:\"medium\", 3:\"low\"}\n",
    "dogs[\"kids\"] = dogs['children'].map(kids)"
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   "cell_type": "markdown",
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   "source": [
    "(sec:eda_distributions)=\n",
    "# What to Look For in a Distribution"
   ]
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   "cell_type": "markdown",
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    "Visual displays of a feature can help us see patterns in observations; they are often much better than direct examination of the numbers or strings themselves. \n",
    "The simple rug plot locates each observation as a \"yarn\" in a\n",
    "\"rug\" along an axis. The rug plot can be useful when we have a handful of observations,\n",
    "but it soon gets difficult to distinguish high-density (most populated) regions\n",
    "with, say, even 100 values. The following figure shows a rug plot with about 150 longevity values for dog breeds along the top of a histogram:"
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  },
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cRhuDvVGcWsGYPX/oQ7q0sBlpzOyZO6TLCE7XQVi6+E9p+Zz94IMPXJ17mZAuVyT8Gi1btozWr19Pw4cP1z0U7cMkMGvWLBWhV69eYUbC4boEXnnlFWrZsiW1bdtW91C0D5PAU089RYMHD6YGDRqEGQmH6xLo3Lkzffjhh5SQkKB7KNqHQeDYsWPUr18/mjNnThhRou/QUi9dixcvpo4dO2pVZunSpfTll1/S888/r3UcGodP4N1331VBHnvssfCDIYIWgdGjR9PNN99Md9xxh9ZxaBw+gd/85jc0aNAgatiwYfjBEEGLAEvXP/7xD0pMTNQ6Do3DI3D48GEaMGAApCs8jN47+qqrrqLdu3drdYyla926dfTiiy9qHYfG4RP485//rII88cQT4QdDBC0CfL3feuutkC4tamYa9+7dm4YMGQLpMoNTK8rdd9+tRrogXVrYwm7M0tW/f396//33w44VTQE8OdKVk5NDsbGx6qbecDdIV7gEI3s8pCuyvP2zQbrk2EO65NhDumTYQ7pkuBfKmpmZSWPGjKGtW7eqn3fo0IFGjRqlvsnG32pr0qRJofYTJ04kHhoOtkG6PFBYjS5AujRgGW4K6TIMVCPc008/Tc8++yxGujSYmWrK7CdMmICRLlNAXcZh6XrppZforbfecnlE6WjmqZGuLVu20P79+6lTp0509uxZevzxx4m/eXjfffdRVlYWdevWjVasWFFAnr/14PRtn1CkC+t0yV3cWKdLjj3W6ZJjj3W65NhjnS4Z9linS4Z70KxTpkyh7Oxseu6554hHwYYOHUrz58/X6imkSwuXeGNIl1wJIF1y7CFdcuwhXTLsIV0y3EvMmp+fr0a2nnnmGXVj79q1a2ngwIHUvXt3Sk1Npfbt21Pt2rULHc/HFN2uvvpq7RvpMdIldzFAuuTYQ7rk2EO65NhDumTYQ7pkuJeY9e2336ZVq1bRjBkz1OJp/MKYO3cuVatWjbZv306LFi2isWPHFloO4s477ywWb+/evbR69WqPnR26AwIgAAIgAAIgUFoIXHLJJa5OxVP3dPl6vHDhQho3bhzNmzePqlevHvBEZs+eTUuWLCH+O9iG6UVX14FnGmGkS64UGOmSY4+RLjn2GOmSYY+RLhnuxbLyjfLDhg2jmTNnBv0mD49e9e3blzIyMiBdHqmdiW5AukxQDC0GpCs0biaOgnSZoBhaDEhXaNzCPQrSFS5BA8dv2LBBLYr5zjvvUOPGjQsi8qJ1fCN9WloaValSRU01jhw5kpKTk4mXjcBIlwH4HgkB6ZIrBKRLjj2kS449pEuGPaRLhnuhrLxA4PLly4v1ZNu2bfTJJ58Qf5uRnwjPi6Z26dJFPSqmpOlHXxBML3qgsBpdgHRpwDLcFNJlGKhGOEiXBizDTSFdhoG6DAfpcglKuhl/KKekpLh+IjykS7pievkhXXq8TLaGdJmkqRdr35FTFBcfT7ExMXoHeqR1xcR4qpQU/hNEJE4H0iVBnQjSJcPdelZIl3XERhNAuozi1AoG6dLCZbQxS9ejMzdR8UVvjKaxEqxeajJNefBGSJcVuqU3KKSrlNYW0hVdhYV0ydUL0iXH3idd+46fketEiJnbNqwB6QqRXVk+DNJVSqsP6YquwkK65OoF6ZJjD+mSY4/pRRn2kC4Z7tazQrqsIzaaANJlFKdWMEiXFi6jjSFdRnFqBYN0aeEy1hjSZQyltwJBurxVD6feQLqcCNnbD+myx9YpMqTLiZC9/ZAue2yDRYZ0yXC3nhXSZR2x0QSQLqM4tYJBurRwGW0M6TKKUysYpEsLl7HGkC5jKL0VCNLlrXo49QbS5UTI3n5Ilz22TpEhXU6E7O2HdNlji5Gu4gQ8+exFk5cApMskTfuxIF32GZeUAdIlxx7SJcce0iXDHiNdMtytZ4V0WUdsNAGkyyhOrWCQLi1cRhtDuozi1AoG6dLCZawxpMsYSm8FgnR5qx5OvYF0ORGytx/SZY+tU2RIlxMhe/shXfbYBosM6ZLhbj0rpMs6YqMJIF1GcWoFg3Rp4TLaGNJlFKdWMEiXFi5jjSFdxlB6KxCky1v1cOoNpMuJkL39kC57bJ0iQ7qcCNnbD+myxxYjXcUJ4Eb6AFfF+fPnKScnhypXrixzNZbhrJAuueJDuuTYQ7rk2EO6ZNhjpEuGu/WsGOmyjthoAkiXUZxawSBdWriMNoZ0GcWpFQzSpYXLWGNIlzGU3goE6fJWPZx6A+lyImRvP6TLHlunyJAuJ0L29kO67LHF9CKmF11dXZhedIXJSiNIlxWsroJCulxhstII0mUFq6ugkC5XmIw3wkiXcaTeCIiRLm/UwW0vIF1uSZlvB+kyz9RtREiXW1Lm20G6zDN1ExHS5YZSFLaBdEVX0SBdcvWCdMmxh3TJsYd0ybCHdMlwt54V0mUdsdEEkC6jOLWCQbq0cBltDOkyilMrGKRLC5exxpAuYyi9FQjS5a16OPUG0uVEyN5+SJc9tk6RIV1OhOzth3TZYxssMqRLhrv1rJAu64iNJoB0GcWpFQzSpYXLaGNIl1GcWsEgXVq4jDWGdBlD6a1AkC5v1cOpN5AuJ0L29kO67LF1igzpciJkbz+kyx5bjHQVJ4AV6QNcFVgyQuZFyFkhXXLsIV1y7CFdcuwhXTLsMdIlw916Vox0WUdsNAGkyyhOrWCQLi1cRhtDuozi1AoG6dLCZawxpMsYSm8FgnR5qx5OvYF0ORGytx/SZY+tU2RIlxMhe/shXfbYYnoR04uuri5ML7rCZKURpMsKVldBIV2uMFlpBOmygtVVUEiXK0zGG2GkyzhSbwTESJc36uC2F5Aut6TMt4N0mWfqNiKkyy0p8+0gXeaZuokI6XJDKQrbQLqiq2iQLrl6Qbrk2EO65NhDumTYQ7pkuFvPCumyjthoAkiXUZxawSBdWriMNoZ0GcWpFQzSpYXLWGNIlzGU3goE6fJWPZx6A+lyImRvP6TLHlunyJAuJ0L29kO67LENFhnSJcPdelZIl3XERhNAuozi1AoG6dLCZbQxpMsoTq1gkC4tXMYaQ7qMofRWIEiXt+rh1BtIlxMhe/shXfbYOkWGdDkRsrcf0mWPLUa6ihPAivQBrgosGSHzIuSskC459pAuOfaQLjn2kC4Z9hjpkuFuPStGuqwjNpoA0mUUp1YwSJcWLqON9x3NpkdnbKR9x88YjRuJYG0b1qApD95IlZLKRSKd8RyQLuNIXQWEdLnCFH2NIF3RVTNIl1y9ol26/r3jsBy8MDNfUimR+v1tM6QrTI6hHA7pCoVa+MdAusJnaCxCTk4OxcbGUrlyxX9z4kLx9F9KSoqrfJAuV5g80wjSJVeKaJeuT7cfpt6zNsgBDCPz/H63QrrC4BfOoZCucOiFfiykK3R2xo7MzMykMWPG0NatW1XMDh060KhRoygpKUn9f+rUqTRp0iQlXM2bNy/4d7AOQLqMlScigSBdEcEcMAmkS449pEuOPaRLhj2kS4Z7oaxbtmyh/fv3U6dOnejs2bP0+OOPU8+ePem+++4j3tenTx/66KOPKDU1lYYMGUK1a9emQYMGBe05pMsDhdXoAqRLA5bhppAuw0A1wkG6NGAZbgrpMgzUZThIl0tQkWw2ZcoUys7Opueee44mT55Mp06dopEjR6oubNy4kQYMGECrVq2CdEWyKJZzQbosAw4SHtIlxx7SJcce0iXDHtIlw73ErPn5+dStWzd65pln6I477qARI0ZQo0aNqFevXuoYfqG0bNmSeEoyLi6uxDgY6fJYYR26A+mSqxekS449pEuOPaRLhj2kS4Z7iVnffvttNYo1Y8YMio+Pp759+1Lr1q3VdCNvPOrVtGlTysjIKLipft26dcXiPfLIIwX3iLk9xQsXLlBubi4lJye7PQTtDBHgL0nwlpiYaCgiwrglwFP6/ForX76820M80y4mJoaW7z5Bfd7b6Jk+6XQk2qVrwv1NKKVcjM4pe6Yt/7JRqVIl4msIW+QI5OXlqXUZmX1p2BISElydhicXR124cCGNGzeO5s2bR9WrV1cnwtOK9evXp0cffVT9/9ChQ9SqVSvauXOn+qajr03Rs547dy59+eWXrmD4GvHFwBYe6NuTWoHQWJsAyy5v/OGPLbIELl68qEaNfa+nyGYPLxv3+/NvT9NvZm8KL5DQ0fP73kr95kTvkhGvd72Gysf89NqNto2/Le/2AzPazs3L/eXZLH7PicZf8gJxrVKliivcnpOuFStW0LBhw2jmzJnUsGHDgpOYNm0aZWVl0ejRo9XPeFRr6NChuKfLVZmjpxGmF+VqhelFOfbRPtKFxVHlrp1ozYzpRQ9UbsOGDfTEE0/QO++8Q40bNy7oEU817dmzh3r06EE8CpaWlkb9+/enunXrKvEKtuGeLg8UVqMLkC4NWIabQroMA9UIB+nSgGW4Ke7pMgzUZThIl0tQNpv17t2bli9fXizFtm3b1BDkrFmzaMKECWp/eno6TZw4kSpXrgzpslmUCMeGdEUYuF86SJcce0iXHHtIlwx7SJcMd+2sWJFeG1lUHQDpkisXpEuOPaRLjj2kS4Y9pEuGu/WsmF60jthoAkiXUZxawSBdWriMNoZ0GcWpFQzSpYXLWGNIlzGU3goE6fJWPZx6A+lyImRvP6TLHlunyJAuJ0L29kO67LENFhnSJcPdelZIl3XERhNAuozi1AoG6dLCZbQxpMsoTq1gkC4tXMYaQ7qMofRWIEiXt+rh1BtIlxMhe/shXfbYOkWGdDkRsrcf0mWPLUa6ihPw3DpdpssP6TJN1G48SJddvsGiQ7rk2EO65NhDumTYY6RLhrv1rJAu64iNJoB0GcWpFQzSpYXLaGNIl1GcWsEgXVq4jDWGdBlD6a1AkC5v1cOpN5AuJ0L29kO67LF1igzpciJkbz+kyx7bYJEhXTLcrWeFdFlHbDQBpMsoTq1gkC4tXEYbQ7qM4tQKBunSwmWsMaTLGEpvBYJ0easeTr2BdDkRsrcf0mWPrVNkSJcTIXv7IV322GKkqzgB3Egf4Ko4f/488ZPnnR4xJHOplu6skC65+kK65NhDuuTYQ7pk2GOkS4a79awY6bKO2GgCSJdRnFrBIF1auIw2hnQZxakVDNKlhctYY0iXMZTeCgTp8lY9nHoD6XIiZG8/pMseW6fIkC4nQvb2Q7rsscX0IqYXXV1dmF50hclKI0iXFayugkK6XGGy0gjSZQWrq6CQLleYjDfCSJdxpN4IiJEub9TBbS8gXW5JmW8H6TLP1G1ESJdbUubbQbrMM3UTEdLlhlIUtoF0RVfRIF1y9YJ0ybGHdMmxh3TJsId0yXC3nhXSZR2x0QSQLqM4tYJBurRwGW0M6TKKUysYpEsLl7HGkC5jKL0VCNLlrXo49QbS5UTI3n5Ilz22TpEhXU6E7O2HdNljGywypEuGu/WskC7riI0mgHQZxakVDNKlhctoY0iXUZxawSBdWriMNYZ0GUPprUCQLm/Vw6k3kC4nQvb2Q7rssXWKDOlyImRvP6TLHluMdBUngBXpA1wVWDJC5kXIWSFdcuwhXXLsIV1y7CFdMuwx0iXD3XpWjHRZR2w0AaTLKE6tYJAuLVxGG0O6jOLUCgbp0sJlrDGkyxhKbwWCdHmrHk69gXQ5EbK3H9Jlj61TZEiXEyF7+yFd9thiehHTi66uLkwvusJkpRGkywpWV0EhXa4wWWkE6bKC1VVQSJcrTMYbYaTLOFJvBMRIlzfq4LYXkC63pMy3g3SZZ+o2IqTLLSnz7SBd5pm6iQjpckMpCttAuqKraJAuuXpBuuTYQ7rk2EO6ZNhDumS4W88K6bKO2GgCSJdRnFrBIF1auIw2hnQZxakVDNKlhctYY0iXMZTeCgTp8lY9nHoD6XIiZG8/pMseW6fIkC4nQvb2Q7rssQ0WGdIlw916VkiXdcRGE0C6jOLUCgbp0sJltDGkyyhOrWCQLi1cxhpDuoyh9FYgSJe36uHUG0iXEyF7+yFd9tg6RYbwWBuIAAAgAElEQVR0ORGytx/SZY8tRrqKE8CK9AGuCiwZIfMi5KyQLjn2h77PpvyYOIqLjZPrRIiZ42Nj6It9J+jJ2RtDjCB7GKRLjj+kS4Y9RrpkuFvPipEu64iNJoB0GcWpFYyla+A/ttG5Cz9qHeeFxldUS6ZO110K6RIoRtuGNWjKgzdSpaRyAtnDTwnpCp9hKBEgXaFQi4JjIF1RUCS/LkK65Orlk661Xx+X60SImTtck0bd0i+DdIXIL5zDIF3h0Cu7x0K6SmntIV3RVVhIl1y9IF1y7DG9KMceI10y7CFdMtytZ4V0WUdsNAGkyyhOrWCQLi1cRhtDuozi1AoG6dLCZawxpMsYSm8FgnR5qx5OvYF0ORGytx/SZY+tU2RIlxMhe/shXfbYBosM6ZLhbj0rpMs6YqMJIF1GcWoFg3Rp4TLaGNJlFKdWMEiXFi5jjSFdmijPnj1LycnJmkdFvjmkK/LMw8kI6QqHXnjHQrrC4xfO0ZCucOiFdyykKzx+oR4N6QpAbvz48VSzZk361a9+VWzvDTfcQAsXLqTLLrssVOYBj8vNzaUJEybQ9OnTaefOnRQbG6vanTt3jpo0aVLomIkTJ1Lnzp2D5od0GS2P9WCQLuuIS0wA6ZJjD+mSYw/pkmEP6dKQru+++47atWtHK1eupEsvvdRYxbKzs6l3795UuXJlWrZsWSHpysrKom7dutGKFSsK8sXHx1NcXPCFHCFdxsoTkUCQrohgDpgE0iXHHtIlxx7SJcMe0uXHfdOmTfTiiy/S/v37KTExkWrUqFGwl0ei9uzZQ02bNqW5c+carVZeXh4tXbqUbrnlFmrWrFkh6crMzKShQ4fS/PnztXJCurRwiTeGdMmVANIlxx7SJcce0iXDHtLlx53lZ9u2bfTaa69RxYoV6eabby7YGxMTQywyLEU80mRj42fAFZWutWvX0sCBA6l79+6UmppK7du3p9q1azumh3Q5IvJUA0iXXDkgXXLsIV1y7CFdMuwhXQG479q1Sz0LLz09PaJVCSRd/MLgkbVq1arR9u3badGiRTR27Fjq2LFjQd8mTZpUrJ9Tp06ljRv1nsfGFwP/KV++fETPG8mILl68qDCUKxedjxSJ5hpmX8inIf/aQdG6In3X9Fr01OxNUVmC+X1vpX5zNtO+42eirv+8Iv34bo0pITb6Hh/FsPl+4aSkpKjjHu0dzs/Pp5ycHDWbVhq2SpUquToNVw+8ZgE5evRosYCXXHIJ8ciX6S2QdBXNMXv2bFqyZAnx376Nb+wvug0YMIC2bNmi1UX+4OdpVLwQtbAZacwvQt4SEhKMxEMQ9wS+P3uRBv9ze9RKV7f0WvQkpMt9wQ21ZOl6s8d1lGxn4sNQL0sOw/cSV6hQwcpnmfXOR3ECnlHjVRCYfWnY3PpCUOliIOPGjaOPP/6Yzpwp/hvYhg0b1E3vpjc30rV69Wrq27cvZWRkBE2P6UXT1bEbD9OLdvkGi47pRTn2mF6UY4/pRRn2mF4MwJ1HkkaNGkXDhw+n6667rthvAnXq1ClY0sFk2QJJF99In5aWRlWqVCF+kYwcOVKtE8bLRgTbIF0mK2M/FqTLPuOSMkC65NhDuuTYQ7pk2EO6AnAfMWIEVa1aVX1rMJJbIOlasGABTZkyhU6dOqXu9+nSpQs99thjVL16dUhXJItjORekyzLgIOEhXXLsIV1y7CFdMuwhXQG4843p33zzjZIdr2z8oZySkuJ6/h0jXV6pnLt+QLrccbLRCtJlg6q7mJAud5xstIJ02aDqHBPSFYDRt99+Sw888AC9+uqragkHt3fnO+OOXAtIV+RYm8gE6TJBMbQYkK7QuJk4CtJlgmJoMSBdoXEL9yhIVwCC7777rlqWoaTN1o304RbT/3hIl0ma9mNBuuwzLikDpEuOPaRLjj2kS4Y9pCsA93379tHBgwdLrEiLFi0cH8MjU87/ZoV0SVdALz+kS4+XydaQLpM09WJBuvR4mWwN6TJJ030sSJcDKwbEm9OzDt0jj0xLSFdkOJvKAukyRVI/DqRLn5mpIyBdpkjqx4F06TMzcQSkqwSK/CxEvqGen7fYr18/6t+/P33xxRfqGYm8bIPXN0iX1ytUuH+QLrl6Qbrk2EO65NhDumTYQ7oCcD9w4AC1bduWunbtSocPH1Y307N08XMZ+WcsX7ykhJc3SJeXq1O8b5AuuXpBuuTYQ7rk2EO6ZNhDugJw59Xos7KyaPLkyeoPP/KHpYsf1XLttdeq5x+y1Hh5g3R5uTqQLi9VB9IlVw1Ilxx7SJcMe0hXAO68OCo/jPKFF14oJF2bNm1SS0msWrVKrRLv5Q3S5eXqQLq8VB1Il1w1IF1y7CFdMuwhXQG48yrwzz//PL355pvEosUrwfN0I9/jxdONixcvlqmWRlZIlwYsDzTF9KJcESBdcuwhXXLsIV0y7CFdAbjn5+erhVFnzJhRaC+Pbk2fPp0aNWokUy2NrJAuDVgeaArpkisCpEuOPaRLjj2kS4Y9pCsId16ZfseOHcTPRKxduzalp6eracdo2CBd0VCl//Yx2qVr77Ez0QW8UG/zacS/ttLar49H3Tl0uCaNuqVfRk/O3hh1fecOQ7rkygbpkmEP6QrAnRdGjY2NLXbfFssXf7PxmmuukamWRlZIlwYsDzSNdunKPJRNd01a6QGS+l346xMt6A+f7YF06aML+whIV9gIQw4A6QoZXVgHQroC4Bs/fjzt37+/2AOvt2/fTvfeey9lZmZ6frFUSFdYr4uIHwzpijjygoSQLjn2kC459pAuGfaQrgDcBw4cSPXr16e+ffsW2utbMuKTTz6hevXqyVTMZVZIl0tQHmkG6ZIrBKRLjj2kS449pEuGPaQrAPfXX39dLQsxf/78QntXr15Nv/71rwkPvJa5WEtzVkiXXHUhXXLsIV1y7CFdMuwhXQG4Z2Rk0P3336/+8FIRtWrVos2bN9N7772n/s/reHl9w0iX1ytUuH+QLrl6Qbrk2EO65NhDumTYQ7pK4L5w4UJ6+eWX6fjx/36jqX379jR69GiqUaOGTLU0skK6NGB5oCmkS64IkC459pAuOfaQLhn2kK4A3Hn6sEqVKnTllVfSsWPH6NSpU3TppZdSSkqKTJVCyArpCgGa4CGQLjn4kC459pAuOfaQLhn2kK4A3IcMGaKWi+C/o3WDdEVX5SBdcvWCdMmxh3TJsYd0ybCHdAXgPnfuXPXIn5UrV1L58uVlKhNmVkhXmAAjfDikK8LA/dJBuuTYQ7rk2EO6ZNhDugJwz87Opn79+hH/3b1792KP/bn++uuxTpfM9Vpqs0K65EoL6ZJjD+mSYw/pkmEP6QrAnRdH/dOf/lRiRbBkhMzFWpqzQrrkqgvpkmMP6ZJjD+mSYQ/pCsA9NzeXLl68WGJFkpKSZKqlkRXTixqwPNAU0iVXBEiXHHtIlxx7SJcMe0iXA3cGxFtcXJxMhULMCukKEZzQYZAuIfBEBOmSYw/pkmMP6ZJhD+kqgfvSpUvVzfR79uxR93f179+fvvjiC+Kfjxw5UqZaGlkhXRqwPNAU0iVXBEiXHHtIlxx7SJcMe0hXAO4HDhxQK8937dqVDh8+TM2aNVPStW3bNvUzlq+qVavKVMxlVkiXS1AeaQbpkisEpEuOPaRLjj2kS4Y9pCsA93HjxlFWVhZNnjxZ/YmJiVHS5Xvg9aJFi4ilxssbpMvL1SneN0iXXL0gXXLsIV1y7CFdMuwhXQG487MVExMT6YUXXigkXZs2baIHHnhAPQybF0/18gbp8nJ1IF1eqg6kS64akC459pAuGfaQrgDcFyxYQM8//zy9+eabxKJVrlw5Nd3I93jxdOPixYtlqqWRFdKlAcsDTTHSJVcESJcce0iXHHtIlwx7SFcA7vn5+fTqq6/SjBkzCu3l0a3p06cXWyxVpnTBs0K6vFiVkvsE6ZKrF6RLjj2kS449pEuGPaQrCPdvv/2WduzYQSdPnqTatWtTenq6mnaMhg3SFQ1V+m8fIV1y9YJ0ybGHdMmxh3TJsId0BeHOcPjCPHv2LF1yySVUoUIFmSqFkBXSFQI0wUMgXXLwIV1y7CFdcuwhXTLsIV0lcJ8zZw699tprdObMmYIWbdq0oVGjRtHll18uUy2NrJAuDVgeaArpkisCpEuOPaRLjj2kS4Y9pCsA98zMTLrnnnvokUceoVtvvZVq1KhBmzdvptmzZ1NCQgJ9/PHHahkJL2+QLi9Xp3jfIF1y9YJ0ybGHdMmxh3TJsId0BeA+bNgwtU7XrFmzCu31LRmxYsUKqlWrlkzFXGaFdLkE5ZFmkC65QkC65NhDuuTYQ7pk2EO6AnDnKUR+1mLRx/0cP36cbrrpJlqzZg3VrFlTpmIus0K6XILySDNIl1whIF1y7CFdcuwhXTLsIV0BuH/++ef09NNP00cffUR16tRRLfLy8mjKlClqYdQPP/ww4tXiQp0/f55SUlJc5YZ0ucLkmUaQLrlSQLrk2EO65NhDumTYQ7oCcF+2bBnxqvQ8slW9enWqV68e7d27V/2/cePGVK1atYKj+L6vdu3ahV293NxcmjBhgloHbOfOnRQbG1sQc+rUqWphVhau5s2bF/w7WFJIV9gliWgASFdEcRdKBumSYw/pkmMP6ZJhD+kKwH3lypXqxnk3G69Uf/3117tpWmKb7Oxs6t27N1WuXJlY+Pyla8uWLdSnTx816paamkpDhgxRa4YNGjQoaE5IV1glifjBkK6IIy9ICOmSYw/pkmMP6ZJhD+mS4V4oK09dLl26lG655RZq1qxZIeniB26fOnWq4P6yjRs30oABA9Q0J0a6PFA8Q12AdBkCGUIYSFcI0AwdAukyBDKEMJCuEKAZOATSZQCiqRC88n1R6eJpzkaNGlGvXr1UGn6htGzZknhZC77Zv6QNI12mqhKZOJCuyHAOlAXSJcce0iXHHtIlwx7SJcM9YNZA0tW3b19q3bo19ezZUx3Do15NmzaljIyMgpvqH3jggWLxeHkL/kKAzsbPnOTN62uQ6ZxTtLSNZvZ8vRzOiae7f78mWnAX6udfn/g5/eGzr2nt18ejrv8drkmjrum16KnZm6Ku79zh+X1vpX5zNtO+4/9dhDpaTqRtwxo0tktDis+7EC1dLtRPnmHxv3c4Kk8iSjtdmtjzOqZutph836ecm9YRahNIunjZivr169Ojjz6qenHo0CFq1apVoSnIEydOFOthixYtVBudLScnh/hPpUqVdA5DWwMEfE8+cPvtVAMpjYbYdeQ03TUp+JS30YQGg2GkyyBMzVDRPtI16YEbqFJivOZZe6P5sWPH1BfF8Et2ZOvBI138We//hbzI9sBsNrfiHjXSNW3aNLVQ6+jRoxWpdevW0dChQ3FPl9nrRjwaphflSgDpkmMf7dI15cEbqVJSOTmAYWTG9GIY8MI4FNOLYcAzfWigka49e/ZQjx49aOHChZSWlkb9+/enunXrKvEKtuGeLtPVsRsP0mWXb7DokC459pAuOfaQLhn2kC4Z7gGzBpIubsiPI+I1vHhLT0+niRMnquUlIF0eKl6YXYF0hQkwjMMhXWHAC/NQSFeYAMM4HNIVBrwwDoV0hQEvkodiRfpI0o58LkhX5Jn7MkK65NhDuuTYQ7pk2EO6ZLhbz4rpReuIjSaAdBnFqRUM0qWFy2hjSJdRnFrBIF1auIw1hnQZQ+mtQJAub9XDqTeQLidC9vZDuuyxdYoM6XIiZG8/pMse22CRIV0y3K1nhXRZR2w0AaTLKE6tYJAuLVxGG0O6jOLUCgbp0sJlrDGkyxhKbwWCdHmrHk69gXQ5EbK3H9Jlj61TZEiXEyF7+yFd9thipKs4AU+u02XyEoB0maRpPxakyz7jkjJAuuTYQ7rk2EO6ZNhjpEuGu/WskC7riI0mgHQZxakVDNKlhctoY0iXUZxawSBdWriMNYZ0GUPprUCQLm/Vw6k3kC4nQvb2Q7rssXWKDOlyImRvP6TLHttgkSFdMtytZ4V0WUdsNAGkyyhOrWCQLi1cRhtDuozi1AoG6dLCZawxpMsYSm8FgnR5qx5OvYF0ORGytx/SZY+tU2RIlxMhe/shXfbYYqSrOAHcSB/gqjh//jzl5OQ4PmJI5lIt3VkhXXL1hXTJsYd0ybGHdMmwx0iXDHfrWTHSZR2x0QSQLqM4tYJBurRwGW0M6TKKUysYpEsLl7HGkC5jKL0VCNLlrXo49QbS5UTI3n5Ilz22TpEhXU6E7O2HdNlji+lFTC+6urowvegKk5VGkC4rWF0FhXS5wmSlEaTLClZXQSFdrjAZb4SRLuNIvREQI13eqIPbXkC63JIy3w7SZZ6p24iQLrekzLeDdJln6iYipMsNpShsA+mKrqJBuuTqBemSYw/pkmMP6ZJhD+mS4W49K6TLOmKjCSBdRnFqBYN0aeEy2hjSZRSnVjBIlxYuY40hXcZQeisQpMtb9XDqDaTLiZC9/ZAue2ydIkO6nAjZ2w/pssc2WGRIlwx361khXdYRG00A6TKKUysYpEsLl9HGkC6jOLWCQbq0cBlrDOkyhtJbgSBd3qqHU28gXU6E7O2HdNlj6xQ5mqWr3+0N6KHmV1D5+Fin0/Tk/nPnztHlqZUpJibGk/0rrZ2CdJXSykK6oquwkC65ekG65NhHu3QlxcfRZzuPygEMI/Or3RpTg5oVIV1hMAzlUEhXKNSi4BhIVxQUya+LkC65ekG65NiXBul6felOOYBhZP50YCtIVxj8Qj0U0hUqOY8fB+nyeIGKdA/SJVcvSJcce0iXHHtIlwx7SJcMd+tZIV3WERtNAOkyilMrGKRLC5fRxpAuozi1gkG6tHAZawzpMobSW4EgXd6qh1NvIF1OhOzth3TZY+sUGdLlRMjefkiXPbbBIkO6ZLhbzwrpso7YaAJIl1GcWsEgXVq4jDaGdBnFqRUM0qWFy1hjSJcxlN4KFIp0XbhwgQ6dyqH4+HhvnYxGb2pVSdJo7Z2mkC65WkC65NhDuuTYQ7pk2EO6ZLhbzxqqdL23/jt6f8N31vtnI8HvH7qRGqZVtBHaekxIl3XEJSaAdMmxh3TJsYd0ybCHdMlwt541HOl6eeEO6/2zkWDpgNaQLhtgXcTMPJRNd01a6aKl95pAuuRqAumSYw/pkmEP6ZLhbj0rpMs6YqMJMNJlFKdWMEiXFi6jjSFdRnFqBYN0aeEy1hjSZQyltwJBurxVD6feQLqcCNnbD+myx9YpMqTLiZC9/ZAue2yDRYZ0yXC3nhXSZR2x0QSQLqM4tYJBurRwGW0M6TKKUysYpEsLl7HGkC5jKL0VCNLlrXo49QbS5UTI3n5Ilz22TpEhXU6E7O2HdNlji5Gu4gRi8vPz82WQRyYrpCsynE1lgXSZIqkfB9Klz8zUEZAuUyT140C69JmZOAIjXSYoejAGpMuDRQnSJUiXXL0gXXLsIV1y7CFdMuwhXTLcrWeFdFlHbDQBpMsoTq1gkC4tXEYbQ7qM4tQKBunSwmWsMaTLGEpvBYJ0easeTr2BdDkRsrcf0mWPrVNkSJcTIXv7IV322AaLDOmS4W49K6TLOmKjCSBdRnFqBYN0aeEy2hjSZRSnVjBIlxYuY40hXcZQ2gl07tw5atKkSaHgEydOpM6dOwdNCOmyUw9bUSFdtsg6x4V0OTOy1QLSZYusc1xIlzMjGy0gXTaoGoyZlZVF3bp1oxUrVhRE5QdSx8XFQbqKEMBjgAxeeJqh8BggTWCGmne4Jo26pV9GT87eaChiZMNAuiLL2z8bpEuGPaRLhrvrrJmZmTR06FCaP3++62O4IUa6tHCJN8ZIl1wJMNIlxx7SJcce0iXDHtIlw9111rVr19LAgQOpe/fulJqaSu3bt6fatWsXOn7Xrl3F4t199920fft213m4IV8Mf/3yIEXvA69b0ZXVk7TO2SuNeRqZt6Sk6Oz/nmPnqOPkVV7BqdUPSJcWLqONIV1GcWoFY+mqWy0632+0TtRjjfPy8ujUqVNUpUoVj/UstO6UK1fO1YFRszjqkSNHaO7cuVStWjUlUYsWLaKxY8dSx44dC070mWeeKXbSn3zyCa1fv94VDF8jnrL8+5bjNGbhDq3jvNJ4cf9bKS0pj6Jx3VsWXt6cpo29wtq/HzExMZR1NoY6/X6NF7vn2Kdol66u6bXoqdmbHM/Tiw3m972V+s3ZTPuOn/Fi94L2qd/tDSgxPo7GL90ZdX3nDn86oBVVL58blX2P5k7z5xO/3/NtQqVhYzdxs0WNdBU9mdmzZ9OSJUuI/w62YXrRzWXgnTaYXpSrRbRLF+7pkrl2WLqS4uPo9WiVroGtqEHNisS/NGGLHAFML0aOtZFMq1evpr59+1JGRgakqwgB3Ehv5BILKQhupA8JW9gH4Ub6sBGGHADSFTK6Mn0gpMvj5ecb6dPS0tT8L081jhw5kpKTk4mXjcBIV2ECkC65ixnSJcMe0iXDnbNCuuTYR3NmSJfHq7dgwQKaMmWKuvGOb1jr0qULPfbYY1S9enVIF0a6PHP1QrpkSgHpkuEO6ZLjHu2ZIV1RUkG+5yclJcX1/Dvu6YqSwv5/N3FPl1y9cE+XHHt8e1GOPZaMkGEP6ZLhbj0rpMs6YqMJIF1GcWoFg3Rp4TLaGNJlFKdWMEiXFi5jjSFdxlB6KxCky1v1cOoNpMuJkL39kC57bJ0iQ7qcCNnbD+myxzZYZEiXDHfrWSFd1hEbTQDpMopTKxikSwuX0caQLqM4tYJBurRwGWsM6TKG0luBIF3eqodTbyBdToTs7Yd02WPrFBnS5UTI3n5Ilz22GOkqTiBqF0d1e5lAutyS8kY7SJdcHSBdcuwhXXLsIV0y7DHSJcPdetayKF3/HtSGGtSsYJ2tjQRnzvz0GBT+hmo0blgyQqZqWDJChjtnjfZ1upYNbk31Uiu4/ka8HOnSlRnSVbrqWXA2ZVW6cnLzorKi+Xl51CA1kRITE6Oy/5AumbJBumS4lw7pakPnLkbn+yXzb1yrklzxw8gM6QoDnpcPLavS1XX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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.histogram(dogs, x=\"longevity\", marginal=\"rug\", nbins=20,\n",
    "             histnorm='percent', width=350, height=250,\n",
    "             labels={'longevity':'Typical lifespan (yr)'},\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we can see an unusually large value that's greater than 16 in the rug plot, it's hard\n",
    "to compare the density of yarns in the other regions. Instead, the histogram gives \n",
    "a much better sense of the density of observations for various longevity values. \n",
    "Similarly, the *density curve* shown in the following figure\n",
    "gives a picture of the regions of high and low density:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "from scipy.stats import gaussian_kde\n",
    "\n",
    "new_x = dogs['longevity'].dropna()\n",
    "bandwidth = 0.2\n",
    "xs = np.linspace(min(new_x), max(new_x), 100)\n",
    "ys = gaussian_kde(new_x, bandwidth)(xs)\n",
    "\n",
    "f2 = go.Figure(go.Scatter(x=xs, y=ys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
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   "source": [
    "f2.update_xaxes(range=[4.5, 18.5], title=\"Typical lifespan (yr)\")\n",
    "f2.update_layout(showlegend=False,width=350, height=250)\n",
    "f2.show()"
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    "In both the histogram and density curve, we can see that the distribution of longevity is asymmetric. There is one main mode around 12 years and a shoulder in the 9–11-year range, meaning\n",
    "that while 12 is the most common longevity, many breeds have a longevity one to three\n",
    "years shorter than 12.  We also see a small secondary mode around 7, and a few\n",
    "breeds with longevity as long as 14 to 16 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When interpreting a histogram or density curve, we examine the symmetry and\n",
    "skewness of the distribution; the number, location, and size of high-frequency\n",
    "regions (modes); the length of tails (often in comparison to a bell-shaped curve);\n",
    "gaps where no values are observed; and unusually large or anomalous values.\n",
    "{numref}`Figure %s <example-density-plot>` provides a characterization of a distribution with several of these\n",
    "features. When we read a distribution, we connect the features that we see in\n",
    "the plot to the quantity measured."
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  },
  {
   "cell_type": "markdown",
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    "```{figure} figures/example-density-plot.png\n",
    "---\n",
    "name: example-density-plot\n",
    "width: 350px\n",
    "---\n",
    "\n",
    "Example density plot identifying qualities of a distribution based on its shape\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another example, the distribution of the number of ailments in dog breed appears in the following histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
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    "bins = [-0.5, 0.5, 1.5, 2.5, 3.5, 9.5]\n",
    "g = sns.histplot(data=dogs, x=\"ailments\", bins=bins, stat=\"density\")\n",
    "g.set(xlabel='Number of ailments', ylabel='density');"
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    "A value of 0 means this breed has no genetic\n",
    "ailments, 1 corresponds to one genetic ailment, and so on. \n",
    "From the histogram, we\n",
    "see that the distribution of ailments is unimodal with a peak at 0. We also\n",
    "see that the distribution is heavily skewed to the right, with a long right tail\n",
    "indicating that few breeds have between four and nine genetic ailments.\n",
    "Although quantitative, ailments is discrete because only a few integer values\n",
    "are possible. For this reason, we centered the bins on the integers so that the\n",
    "bin from 1.5 to 2.5 contains only those breeds with two ailments. We also made\n",
    "the rightmost bin wider. We lumped into one bin all of the breeds with four to\n",
    "nine ailments. When bin counts are small, we use wider bins to further smooth\n",
    "the distribution because we do not want to read too much into the fluctuations\n",
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    "Next, we point out three key aspects of histograms and density curves: the $y$-axis should be on a density scale; smoothing hides unimportant details; and histograms are fundamentally different from bar plots. We describe each in turn. "
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    "Density in the y-axis\n",
    ": The y-axes in the histograms of longevity and ailments are both labeled \"density.\" \n",
    "This label implies that the total area of the bars in the\n",
    "histogram equals 1. To explain, we can think of the histogram as a skyline with tall buildings having denser populations, and we find the fraction of observations in any bin from the area of the rectangle.\n",
    "For example, the rectangle that runs\n",
    "from 3.5 to 9.5 in the ailments histogram contains about 10% of the breeds: 6 (width) × 0.017 (height) is roughly 0.10. If all of the bins are the same width, then the\n",
    "\"skyline\" will look the same whether the y-axis represents counts or density.\n",
    "But changing the y-axis to counts in this histogram\n",
    "would give a misleading picture of a very large rectangle in the right tail."
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    "Smoothing\n",
    ": With a histogram we hide the details of individual yarns in a rug plot in order\n",
    "to view the general features of the distribution. Smoothing refers to this\n",
    "process of replacing sets of points with rectangles; we choose not to show\n",
    "every single point in the dataset in order to reveal broader trends. We might\n",
    "want to smooth out these points because this is a sample and we believe that\n",
    "other values near the ones we observed are reasonable; and/or we want to focus\n",
    "on general structure rather than individual observations. Without the rug, we\n",
    "can't tell where the points are in a bin.\n",
    "Smooth density curves, like the one we showed earlier for longevity, also have the property that the total area under the\n",
    "curve sums to 1. The density curve uses a smooth *kernel* function to spread out the\n",
    "individual yarns and is sometimes referred to as a *kernel density estimate* or KDE for short."
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    "Bar plot ≠ histogram\n",
    ": With qualitative data, the bar plot serves a similar\n",
    "role to the histogram. The bar plot gives a visual presentation of the\n",
    "\"popularity\" or frequency of different groups. However, we cannot interpret the\n",
    "shape of the bar plot in the same way as a histogram. Tails and symmetry do not\n",
    "make sense in this setting. Also, the frequency of a category is represented by\n",
    "the height of the bar, and the width carries no information.\n",
    "The two bar charts that follow display identical information about\n",
    "the number of breeds in a category; the only difference is in the width of the bars.\n",
    "In the extreme, the rightmost plot eliminates the bars entirely and represents each count by a single dot. (Without the connecting lines, this figure is called a _dot plot_.) Reading this line plot, we see that only a few breeds are unsuitable for children:"
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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    "kid_counts = dogs.groupby(['kids']).count()\n",
    "kid_counts = kid_counts.reindex([\"high\", \"medium\", \"low\"])"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = make_subplots(rows=1, cols=3)\n",
    "\n",
    "fig.add_trace(go.Bar(x=kid_counts.index, y=kid_counts['breed']), row=1, col=1)\n",
    "\n",
    "fig.add_trace(go.Bar(x=kid_counts.index, y=kid_counts['breed']), row=1, col=2)\n",
    "fig.update_traces(width=0.1, row=1, col=2)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=kid_counts.index, y=kid_counts['breed'],\n",
    "                        mode='markers+lines'), row=1, col=3)\n",
    "\n",
    "fig.update_xaxes(title='Breed suitable for kids', row=1, col=2)\n",
    "fig.update_yaxes(title='count', row=1, col=1)\n",
    "\n",
    "fig.update_yaxes(range=[0,70])\n",
    "fig.update_layout(showlegend=False,width=650, height=250)              \n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have covered how to examine distributions of single features, we\n",
    "turn to the situation when we want to look at two features and how they relate."
   ]
  }
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